Design of vision system to recognize the scribed number on the wafer.
DESIGN VISION SYSTEM TO RECOGNIZE THE SCRIBED NUMBER ON THE WAFER
LIAW GEOK PHENG
A report submitted in partial fulfillment of the requirements for the degree of Bachelor of Electrical Engineering (Control, Instrument and Automation)
Faculty of Electrical Engineering
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
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“I hereby declare that I have read through this report entitle “Design of Vision System to
Recognized the Scribed Number on the Wafer” and found that it has comply the partial fulfilment for awarding the degree of Bachelor of Electrical Engineering (Control,
Instrumentation and Automation)”
Signature : ………
Supervisor’s Name : Mr.Lim Wee Teck
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I declare that this report entitle “Design of Vision System to Recognized the Scribed Number on the Wafer” is the result of my own research except as cited in the references. The report has not been accepted for any degree and is not concurrently submitted in candidature of any other degree.
Signature : ...
Name : ...
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ACKNOWLEDGEMENT
In preparing this report, I was in contact with many people, researchers, academicians and practitioners. They have contributed towards my understanding and thought. In particular, I wish to express my sincere appreciation to my main project supervisor, Mr. Lim Wee Teck, for encouragement, guidance critics and friendship. I am also very thankful to my panel Pn. Saleha Binti Mohamad Saleh and Dr. Lim Kim Chuan for their guidance, advices and motivation. Without their continued support and interest, this project would not have been same as presented here. My sincere appreciation also extends to all my classmate and others who have provided assistance at various occasions. Their views and tips are useful indeed. Lastly, I am grateful to all my family members.
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ABSTRACT
This project is carry on as industrial implementation with Silterra. Sdn. Bhd. The company existing vision machine very expensive and the sample being scanned and stored in the database unable to modified or editable. The company requested to design vision system to replace the existing one. Therefore, the objective of the project is to design a vision system by using Matlab Simulink to recognize the scribed number on the wafer and store the detected scribed number in Excel file. Due to unable to physically test at the company, therefore the testing sample and hardware setup to mimic the environment at the production line should be considered. Besides that, the theory for techniques of Optical Character Recognition (OCR) and previous work related to project are studied and a concept of designing the vision system is carried out. To validate the result, the hardware setup to mimic the environment at the Silterra production line and testing sample both are considered. Besides being valid, a test should also be reliable, to ensure the vision system able to perform well, three experiments are carry out and each experiment are tested by 100 good and 100 not good testing samples which is to identify the best image size, median filter, and the matching percentage respectively. For the confirmation test, with the best identified parameter from previous experiment, to test others 100 good and 100 not good testing sample to the system in the existing of white and yellow color of light to ensure system can produce up to 95% of recognition, and this also can identify whether the final result will or will not be affected by color of light. Based on the result finding, the vision system design actually can have up to 100% of recognition, and cheaper price compare to the existing vision machine at the company. As the conclusion, hopefully the vision system design can really apply at Silterra Sdn. Bhd.
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ABSTRAK
Projek ini berjalan seperti pelaksanaan industri dengan Silterra. Sdn. Bhd syarikat yang sedia ada mesin visi yang sangat mahal dan sampel scan dan disimpan dalam pangkalan data tidak dapat diubah atau disunting. Syarikat itu meminta untuk mereka bentuk sistem penglihatan untuk menggantikan yang sedia ada. Oleh itu, objektif projek ini adalah untuk merekabentuk satu sistem penglihatan dengan menggunakan Matlab Simulink untuk mengenali nombor tersebut scribed pada wafer dan menyimpan nombor scribed yang dikesan dalam fail Excel. Oleh kerana tidak dapat fizikal menguji di syarikat itu, oleh itu sampel ujian dan persediaan perkakasan untuk meniru alam sekitar di barisan pengeluaran perlu dipertimbangkan. Selain itu, teori untuk teknik Aksara Optik (OCR) dan kerja-kerja sebelum ini yang berkaitan dengan projek yang dikaji dan konsep mereka bentuk sistem penglihatan yang dijalankan. Untuk mengesahkan keputusan, persediaan perkakasan untuk meyerupai alam sekitar di barisan pengeluaran Silterra dan sampel ujian kedua-dua akan dipertimbangkan. Selain menjadi sah, ujian juga perlu boleh dipercayai, untuk memastikan sistem penglihatan yang dapat menunjukkan prestasi yang baik, tiga eksperimen yang menjalankan dan setiap uji kaji diuji oleh 100 baik dan 100 sampel ujian tidak baik iaitu untuk mengenal pasti saiz imej yang terbaik, iaitu median menapis, dan peratusan yang hampir sama masing-masing. Untuk ujian pengesahan, dengan parameter yang terbaik yang dikenal pasti daripada eksperimen sebelum ini, untuk menguji dengan mengunakan 100 baik dan 100 sampel ujian tidak baik kepada sistem yang sedia ada dalam keadaan yang bercahaya warna putih dan kuning untuk memastikan sistem boleh menghasilkan sehingga 95% daripada pengiktirafan, dan ini juga boleh mengenal pasti sama ada keputusan akhir akan atau tidak akan terjejas oleh warna cahaya. Berdasarkan penemuan hasil, reka bentuk sistem penglihatan sebenarnya boleh mempunyai sehingga 100% daripada pengiktirafan, dan harga lebih murah berbanding dengan mesin yang sedia ada di syarikat itu. Sebagai kesimpulan, diharapkan reka bentuk sistem penglihatan boleh diaplikasikan di Silterra Sdn. Bhd
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TABLE OF CONTENT
CHAPTER TITLE PAGE
ACKNOWLEDGEMENT ii
ABSTRACT iii
ABSTRAK iv
TABLE OF CONTENT vi
LIST OF FIGURES ix
LIST OF TABLES xii
LIST OF APPENDICES xiv
1 INTRODUCTION 1
1.1 Motivation 2
1.2 Problem Statement 3
1.3 Objective 3
1.4 Scope 3
2 LITERATURE REVIEW 4
2.1 Journals Literature 4
2.1.1 Automatic License Plate Recognition 2.1.2 Recognition based on document
2.1.3 Optical Character Recognition on Product Item
6 8 10 2.2 Summary Previous Works Related To Project 11
2.3 Journal Theory Literature 14
2.3.1 Image Acquisition 15
2.3.2 Preprocessing 19
2.3.3 Segmentation 21
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2.3.5 Recognition 24
2.3.6 Post-processing 24
2.4 Proposed Model After Studied the Previous Work Related to Project and Theory
25
2.4.1 Image Acquisition 25
2.4.2 Preprocessing 26
2.4.3 Segmentation and Character Extraction 26
2.4.4 Recognition 29
2.4.5 Post-processing 29
3 METHODOLOGY 30
3.1 Test Validity and Reliability 31
3.2 Validity 32
3.2.1 Hardware Setup 33
3.2.2 Sample Preparation 35
3.3 Technique 38
3.3.1 Training Phase 43
3.3.2 Matching Phase 53
3.4 Reliability of data 53
3.4.1 Experiment 1 55
3.4.2 Experiment 2 56
3.4.3 Experiment 3 57
3.4.4 Confirmation Test 58
4 RESULT AND DISCUSSION 73
5 CONCLUSION AND RECOMMENDATION 74
REFERENCES 76
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LIST OF FIGURES
FIGURE TITLE PAGE
1.1 The existing tracking process in Silterra Sdn. Bhd. 2 1.2 After implement the proposed low cost vision system 2
2.1 Phases of OCR technique 14
2.2 Conventional preprocessing steps 15
2.3 Color Image 15
2.4 Grey image 15
2.5 Original ship image 16
2.6 After low pass filtering image 16
2.7 Intensity Image 17
2.8 Noise applied 17
2.9 Median Filter applied 17
2.10 Method for binarization 17
2.11 Before Otsu’ method 18
2.12 After Otsu’ method 18
2.13 Before Optimal threshold 18
2.14 After Optimal threshold 18
2.15 (a) a skewed typewritten text; (b) skew corrected image 19
2.16 (a) Original pattern; (b) result of thinning 19
2.17 Strategies for character segmentation 20
2.18 Line segmentation 20
2.19 Word segmentation 21
2.20 Character Segmentation 21
2.21 Three feature extraction method 22
2.22 Before and after the edge detection 22
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2.24 After normalization in each difference size of character will become same size.
23 2.25 The proposed OCR model to designing the vision system. 25
2.26 The sample ‘ABC123’ 25
2.27 The process in preprocessing. 26
2.28 The segmented character in 30x20 size image 26
2.29 Generic of Character Recognition Model 27
2.30 (a), (b), (c), (d), (e) shows that the white pixel left after match between the Sample and Template
28 2.31 The process to recognize the character with the range of pixel
difference
29
3.1 The flow chart of project 31
3.2 Overview flow to carry out validity and reliability test 31 3.3 The hardware setup to mimic the environment in the Silterra Sdn.
Bhd. manufacturing plant
32
3.4 White color of light 33
3.5 Yellow color of light 33
3.6 The sample for creating the template 34
3.7 The two set of good testing sample(G sample) 34
3.8 The two set of not good testing sample(NG sample) 34 3.9 A group of not good(NG) sample printed on 80mg paper 35 3.10 A group of good(G) sample printed on 80mg paper 35
3.11 The proposed OCR technique to vision system. 36
3.12 The methodology for vision system design. 37
3.13 Simulink block diagram for template saving in training phase. 38 3.14 Simulink block diagram for recognition process in matching phase. 44 3.15 Simulink block for recognition of sample B when the sample is
matching with template B.
49 3.16 Simulink block for recognition of sample C when the sample is
matching with template
49
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LIST OF TABLES
TABLE TITLE PAGE
2.1 The OCR techniques used by Muhammad Sarfraz, Mohammed Jameel Ahmed, and Syed A. Ghazi to recognize the license plate.
5 2.2 The OCR techniques used by Serkan Ozbay, and Ergun Ercelebi to
recognize the license plate.
5 2.3 The OCR techniques used by Kumar Parasuraman and P.Vasantha
Kumar to recognize the license plate.
6 2.4 The OCR techniques used by G.Vamvakas, B.Gatos, N.
Stamatopoulos, and S.J.Perantonis to recognize the historical documents.
7
2.5 The OCR techniques used by Junaid Tariq, Umar Nauman, and Muhammad Umair Naru to recognize for name cards reading.
7 2.6 The OCR techniques used by Teresa Vania Tjahja, Anto Satriyo
Nugroho, Nur Aziza Azis, Rose Maulidiyatul Hikmah, and James Purnama to recognize for (IADR)
8
2.7 The OCR techniques used by Ernest Valveny, and Antonio L´opez to recognize for numerical information in sachets with surgical material
9
2.8 The OCR techniques used by Huihuang. Zhao, and Zhaohua. Wu to recognize characters in surface mount technology (SMT) product
9 2.9 The OCR techniques used by Rakhi P. Ghugardare, Sandip P.
Narote, P. Mukherji, and Prathamesh M. Kulkarni to recognize Temperature Monitoring System
10
2.10 Summary of all the previous work related to the project and the proposed model that implement to the project after studied all the previous work.
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3.1 The description for each process in training phase 39 3.2 The list of template need to save in the system. (Kindly refer to
Appendix B).
443
3.3 The description for each process. 45
3.4 The difference result between more and less than 90 white pixel left. 49
3.5 The list for declaration of character. 50
3.6 The white pixel left after match for character from 'A' to 'Z' and '0' to '9'
54 3.7 The comparison between the percentages of recognition with
difference image size
55 3.8 The comparison between the percentages of recognition with
difference median filter.
56 3.9 The comparison between the percentages of recognition with
difference matching percentages.
57 4.1 The comparison between the percentages of recognition with
difference image size.
60 4.2 The list of character unable to read with difference image size 61 4.3 The comparison between the percentages of recognition with
difference matrix of median filter.
62 4.4 The list of character unable to read and character wrong recognition
with difference matrix of median filter
63 4.5 The comparison between the image with different matrix of median
filter.
64 4.6 The comparison between the percentages of recognition with
difference matching percentage.
66 4.7 The list of character unable to read and character wrong recognition
with difference matric of median filter.
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4.8 The comparison between the percentages of recognition after adjustment of white pixel value left at character C, F, and 3 in white light condition.
68
4.9 The comparison between the percentages of recognition after adjustment of white pixel value left at character C, F, and 3 in yellow light condition.
69
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LIST OF APPENDICES
APPENDICES TITLE PAGE
A1 100 good(G) testing sample. 79
A2 100 not good(NG) testing sample. 80
B1 The original template with white color light. 81
B2 The original template with yellow color light. 83 B3 The template with different image size and median filter with
white color light stored in database of the vision system.
85 B4 The template with different image size and median filter with
yellow color light stored in database of the vision system.
101 C White pixel value left after match for all the character and
alphabet.
118 D The result for the comparison between the percentages of
recognition after matching with 100 good(G) sample to the template with difference image size when the 3x3 median filter and 86% of matching percentage is fixed and in white light condition.
119
E The result for the comparison between the percentages of recognition after matching with 100 good(G) sample to the template with difference matrix of median filter when the 30x20 of image size and 86% of matching percentage is fixed and in white light condition.
121
F1 The result for the comparison between the percentages of recognition after matching with 100 good(G) sample to the template with difference matching percentage when the 30x20 of image size and 3x3 of median filter is fixed and in white light condition.
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F2 The result for the comparison between the percentages of recognition after matching with 100 not good(NG) sample to the template with difference matching percentage when the 30x20 of image size and 3x3 of median filter is fixed and in white light condition.
125
G1 The comparison between the percentages of recognition after adjustment of white pixel value left at character C, F, and 3 in white light condition with the three best parameter for 100 good(G) and 100 not good(NG) testing sample.
127
G2 The comparison between the percentages of recognition after adjustment of white pixel value left at character C, F, and 3 in yellow light condition with the three best parameter for 100 good(G) and 100 not good(NG) testing sample.
129
H Gantt Chart 131
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CHAPTER 1
INTRODUCTION
This chapter will discuss the background of machine vision with Optical Character Recognition; problem statement, objective and scope of the project.
1.1Motivation
Machine vision provided important advantage in term of cost and precision. It has been proven successful in controlled environment such as factory production line to ensure repeatability and reproducibility in good quality control, and can run continuously without rest. However, the existing sorting machine in Silterra manufacturing plant is used as a wafer tracking process to scan and sort the wafer base on the scribed number and this can help the workers to determine the wafer position. Unfortunately, the sorting machine is very expensive (around RM600 000).Therefore, this causes the limited number of sorting machine available in the production line.
Furthermore, the large quantities of wafer produced in short period with good quality which can saving cost and time. Due to limited number of sorting machine available in the production line, this will take a long time when large quantities of wafer need to process, and if any system breakdown will causes loss to the company in both cost and time. Figure 1.1 shows the existing tracking process in Silterra Sdn. Bhd.
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Figure 1.1: The existing tracking process in Silterra Sdn. Bhd.
For this reason, a low cost vision system design with Optical Character Recognition is created to increase the number of sorting machine in industry, especially dealing with high volume of wafer produced. Figure 1.2 shows after implement the low cost vision system design.
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1.2Problem Statement
As a semiconductor manufacturing company, Silterra Sdn. Bhd produces microchip and the wafer processing are one of the existing process in the production. The existing sorting machine are used to scan the scribed number on wafer and store data to an Excel file for wafer tracking process to determine the position of the wafer. But the machine is very expensive (around RM 600,000) and causes limited number of machine available in the manufacturing plant, therefore the time taken of the scanning process will be long and if any breakdown will directly cost the company in both money and time.
1.3Objective
The aim of this project are:
1) To design a vision system to recognize the scribed number on the wafer. 2) To store the detected scribed number to Excel file.
1.4Scope
The scope of this project are the vision system design by using Matlab Simulink and low cost Logitech Webcam HD C615 (RM 200) to captured the image. Due to unable to get the real wafer sample, therefore to prepare the printed sample based on standard SME M12/M13. Required percentage of recognition up to 95%. To mimic the environment illuminated by white and yellow light. The prototype with the size of 18cm width x10.5cm height and the distance between the webcam and sample is 5cm.
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CHAPTER 2
LITERATURE SURVEY AND PROJECT BACKGROUND
This chapter discuss the summary of previous work related to the project, the theory of OCR technique and proposed model of the vision system design.
2.1 Journal Literature
The comparison between all the previous works related to project such as Optical Character Recognition for Automatic License Plate Recognition, Optical Character Recognition on document reading, and Optical Character Recognition on surface product item. All this previous work are very helpful by giving a lot of ideas and knowledge which can implement and apply to the project.
2.1.1 Automatic License Plate Recognition
In 2003, Muhammad Sarfraz, Mohammed Jameel Ahmed, and Syed A. Ghazi et al.[1] presented the license plate recognition methods as shown in Table 2.1. Recognition rate proved to be 96.22% for the extraction of plate region, 94.04% for the segmentation of the characters and 95.24% for the recognition unit accurate, giving the overall system performance 95% recognition rate. This approach having some problem in extracting the plate, diplomatic cars and military vehicles, are not addressed since they are rarely seen. Detection only for white, black, red, and green color plate or numbers.
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In 2005, Serkan Ozbay, and Ergun Ercelebi et al.[2] recognized OCR techniques as shown in Table 2.2. Final output it is proved to be 97.6% for the extraction of plate region, 96% for the segmentation of the characters and 98.8% for the recognition unit accurate, giving the overall system performance 92.57% recognition rate. This system is designed for the identification of Turkish license plates only.
Table 2.2: The OCR techniques used by Serkan Ozbay, and Ergun Ercelebi to recognize the license plate.
Acquisition processing Extraction processing
Digital camera
Vertical edge detection by sobel algorithm, Filtering by seed filling algorithm, Vertical Edge Matching Normalization, Template matching Image Acquisition Pre- processing
Segmentation Feature Extraction
Recognition Post-processing Smearing algorithms, Filtering, Morphological algorithms Edge detection algorithms, Smearing algorithms Template matching
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In 2010, Kumar Parasuraman and P.Vasantha Kumar et al. [3] proposed an algorithm consists of three major parts as shown in Table 2.3. The overall system performance 98% recognition rate. The proposed method is mainly designed for real-time Malaysian license plate, and can be readily extended to cope with license plates of other countries, especially those using Latin characters.
Table 2.3: The OCR techniques used by Kumar Parasuraman and P.Vasantha Kumar to recognize the license plate.
2.1.2 Optical Character Recognition on document reading
In 2010, G.Vamvakas, B.Gatos, N. Stamatopoulos, and S.J.Perantonis et al. [4] proposed a methodology for recognizing historical documents as shown in Table 2.4. The overall system performance 98.4% of recognition for the test line detection rate, 98% for the text line recognition accuracy and 97% for the word segmentation detection rate, 90.3% for word segmentation recognition accuracy, giving the overall system performance 95.8% recognition rate. This methodology can be applied to either machine printed or handwritten documents. It requires neither any knowledge of the fonts nor the existence of standard database because it can adjust depending on the type of documents that want to process.
Image Acquisition
Pre- processing
Segmentation Feature Extraction
Recognition Post-processing
Thinning Vertical and horizontal projection
Edge detection algorithm, Vertical projection
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S.J.Perantonis to recognize the historical documents.
In 2010, Junaid Tariq, Umar Nauman, and Muhammad Umair Naru et al. [5] presented a simple, efficient, and less costly approach to construct OCR for cards reading or any document that has fix font size and style are shown in Table 2.5. Line extraction accuracy is 100%. Character extraction accuracy is 100%. The accuracy result of both the hard matching and soft matching (2 value range) also 100% but in soft matching (4 value matching) is 96.15%. It having some limitation like to use soft matching. For example: character explanation mark "!" might be matched with character capital "L" or small letter "I".
Table 2.5: The OCR techniques used by Junaid Tariq, Umar Nauman, and Muhammad Umair Naru to recognize for name cards reading.
Image Acquis ition
Pre- processing
Segmentation Feature Extraction
Recognition Post-processing Binarization and Enhancement Text line segmentation, Word and character segmentation Normalization, Zoning, Upper and lower character profiles, Left and right character profiles Radial basis function (RBF) kernel Image Acquis ition Pre- processing
Segmentation Feature Extraction
Recognition Post-processing
scanner Binarization Line
segmentation, Character segmentation "height", "width", and ''checksum'' Hard matching, Soft matching
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Figure 1.1: The existing tracking process in Silterra Sdn. Bhd.
For this reason, a low cost vision system design with Optical Character Recognition is created to increase the number of sorting machine in industry, especially dealing with high volume of wafer produced. Figure 1.2 shows after implement the low cost vision system design.
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1.2Problem Statement
As a semiconductor manufacturing company, Silterra Sdn. Bhd produces microchip and the wafer processing are one of the existing process in the production. The existing sorting machine are used to scan the scribed number on wafer and store data to an Excel file for wafer tracking process to determine the position of the wafer. But the machine is very expensive (around RM 600,000) and causes limited number of machine available in the manufacturing plant, therefore the time taken of the scanning process will be long and if any breakdown will directly cost the company in both money and time.
1.3Objective
The aim of this project are:
1) To design a vision system to recognize the scribed number on the wafer. 2) To store the detected scribed number to Excel file.
1.4Scope
The scope of this project are the vision system design by using Matlab Simulink and low cost Logitech Webcam HD C615 (RM 200) to captured the image. Due to unable to get the real wafer sample, therefore to prepare the printed sample based on standard SME M12/M13. Required percentage of recognition up to 95%. To mimic the environment illuminated by white and yellow light. The prototype with the size of 18cm width x10.5cm height and the distance between the webcam and sample is 5cm.
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CHAPTER 2
LITERATURE SURVEY AND PROJECT BACKGROUND
This chapter discuss the summary of previous work related to the project, the theory of OCR technique and proposed model of the vision system design.
2.1 Journal Literature
The comparison between all the previous works related to project such as Optical Character Recognition for Automatic License Plate Recognition, Optical Character Recognition on document reading, and Optical Character Recognition on surface product item. All this previous work are very helpful by giving a lot of ideas and knowledge which can implement and apply to the project.
2.1.1 Automatic License Plate Recognition
In 2003, Muhammad Sarfraz, Mohammed Jameel Ahmed, and Syed A. Ghazi et al.[1] presented the license plate recognition methods as shown in Table 2.1. Recognition rate proved to be 96.22% for the extraction of plate region, 94.04% for the segmentation of the characters and 95.24% for the recognition unit accurate, giving the overall system performance 95% recognition rate. This approach having some problem in extracting the plate, diplomatic cars and military vehicles, are not addressed since they are rarely seen. Detection only for white, black, red, and green color plate or numbers.
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Table 2.1: The OCR techniques used by Muhammad Sarfraz, Mohammed Jameel Ahmed, and Syed A. Ghazi to recognize the license plate.
In 2005, Serkan Ozbay, and Ergun Ercelebi et al.[2] recognized OCR techniques as shown in Table 2.2. Final output it is proved to be 97.6% for the extraction of plate region, 96% for the segmentation of the characters and 98.8% for the recognition unit accurate, giving the overall system performance 92.57% recognition rate. This system is designed for the identification of Turkish license plates only.
Table 2.2: The OCR techniques used by Serkan Ozbay, and Ergun Ercelebi to recognize the license plate.
Image Acquisition
Pre- processing
Segmentation Feature Extraction
Recognition
Post-processing
Digital camera
Vertical edge detection by sobel algorithm, Filtering by seed filling algorithm, Vertical Edge Matching Normalization, Template matching Image Acquisition Pre- processing
Segmentation Feature
Extraction
Recognition
Post-processing Smearing algorithms, Filtering, Morphological algorithms Edge detection algorithms, Smearing algorithms Template matching
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In 2010, Kumar Parasuraman and P.Vasantha Kumar et al. [3] proposed an algorithm consists of three major parts as shown in Table 2.3. The overall system performance 98% recognition rate. The proposed method is mainly designed for real-time Malaysian license plate, and can be readily extended to cope with license plates of other countries, especially those using Latin characters.
Table 2.3: The OCR techniques used by Kumar Parasuraman and P.Vasantha Kumar to recognize the license plate.
2.1.2 Optical Character Recognition on document reading
In 2010, G.Vamvakas, B.Gatos, N. Stamatopoulos, and S.J.Perantonis et al. [4] proposed a methodology for recognizing historical documents as shown in Table 2.4. The overall system performance 98.4% of recognition for the test line detection rate, 98% for the text line recognition accuracy and 97% for the word segmentation detection rate, 90.3% for word segmentation recognition accuracy, giving the overall system performance 95.8% recognition rate. This methodology can be applied to either machine printed or handwritten documents. It requires neither any knowledge of the fonts nor the existence of standard database because it can adjust depending on the type of documents that want to process.
Image Acquisition
Pre- processing
Segmentation Feature
Extraction
Recognition
Post-processing
Thinning Vertical and horizontal projection
Edge detection algorithm, Vertical projection
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Table 2.4: The OCR techniques used by G.Vamvakas, B.Gatos, N. Stamatopoulos, and S.J.Perantonis to recognize the historical documents.
In 2010, Junaid Tariq, Umar Nauman, and Muhammad Umair Naru et al. [5] presented a simple, efficient, and less costly approach to construct OCR for cards reading or any document that has fix font size and style are shown in Table 2.5. Line extraction accuracy is 100%. Character extraction accuracy is 100%. The accuracy result of both the hard matching and soft matching (2 value range) also 100% but in soft matching (4 value matching) is 96.15%. It having some limitation like to use soft matching. For example: character explanation mark "!" might be matched with character capital "L" or small letter "I".
Table 2.5: The OCR techniques used by Junaid Tariq, Umar Nauman, and Muhammad Umair Naru to recognize for name cards reading.
Image Acquis ition
Pre- processing
Segmentation Feature
Extraction
Recognition
Post-processing Binarization and Enhancement Text line segmentation, Word and character segmentation Normalization, Zoning, Upper and lower character profiles, Left and right character profiles Radial basis function (RBF) kernel Image Acquis ition Pre- processing
Segmentation Feature
Extraction
Recognition
Post-processing
scanner Binarization Line
segmentation, Character segmentation "height", "width", and ''checksum'' Hard matching, Soft matching